ISCAS OpenIR
Mining explicit rules for software process evaluation
Sun, Chengnian (1); Du, Jing (2); Chen, Ning (1); Khoo, Siau-Cheng (1); Yang, Ye (2)
2013
Conference Name2013 International Conference on Software and Systems Process, ICSSP 2013
Pages118-125
Conference DateMay 18, 2013 - May 19, 2013
Conference PlaceSan Francisco, CA, United states
Indexed TypeEI
Publish PlaceAssociation for Computing Machinery, General Post Office, P.O. Box 30777, NY 10087-0777, United States
ISBN9781450320627
Department(1) School of Computing, National University of Singapore, Singapore; (2) Lab for Internet Software Technologies, Institute of Software, Chinese Academy of Sciences, China
English AbstractWe present an approach to automatically discovering explicit rules for software process evaluation from evaluation histories. Each rule is a conjunction of a subset of attributes in a process execution, characterizing why the execution is normal or anomalous. The discovered rules can be used for stakeholder as expertise to avoid mistakes in the future, thus improving software process quality; it can also be used to compose a classifier to automatically evaluate future process execution. We formulate this problem as a contrasting itemset mining task, and employ the branch-and-bound technique to speed up mining by pruning search space. We have applied the proposed approach to four real industrial projects in a commercial bank. Our empirical studies show that the discovered rules can precisely pinpoint the cause of all anomalous executions, and the classifier built on the rules is able to accurately classify unknown process executions into the normal or anomalous class. Copyright 2013 ACM.; We present an approach to automatically discovering explicit rules for software process evaluation from evaluation histories. Each rule is a conjunction of a subset of attributes in a process execution, characterizing why the execution is normal or anomalous. The discovered rules can be used for stakeholder as expertise to avoid mistakes in the future, thus improving software process quality; it can also be used to compose a classifier to automatically evaluate future process execution. We formulate this problem as a contrasting itemset mining task, and employ the branch-and-bound technique to speed up mining by pruning search space. We have applied the proposed approach to four real industrial projects in a commercial bank. Our empirical studies show that the discovered rules can precisely pinpoint the cause of all anomalous executions, and the classifier built on the rules is able to accurately classify unknown process executions into the normal or anomalous class. Copyright 2013 ACM.
Language英语
Content Type会议论文
URIhttp://ir.iscas.ac.cn/handle/311060/16645
Collection中国科学院软件研究所
Recommended Citation
GB/T 7714
Sun, Chengnian ,Du, Jing ,Chen, Ning ,et al. Mining explicit rules for software process evaluation[C]. Association for Computing Machinery, General Post Office, P.O. Box 30777, NY 10087-0777, United States,2013:118-125.
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